FiGVCL: Fine-Grained Benchmark and Method for Video Copy Localization

Content-based video copy localization (VCL) aims to detect and locate copied segments in pairs of videos. VCL requires fine-grained video analysis to robustly identify copied segments that have been edited. Despite recent progress, the prohibitive cost of annotating copied segments and the lack of a fine-grained benchmark hinder the development of effective VCL systems. In this work, we annotate a new real-world dataset, FiGVCL, with challenging scenarios designed to evaluate VCL methods. FiGVCL is carefully annotated to preserve the temporal correspondences observed in copied segments. Moreover, we propose a novel fine-grained VCL benchmark metric based on temporal correspondences to improve discriminability. Finally, we design a simple but effective baseline model that uses fine-grained local embeddings for accurate copied segment localization. We also present an unsupervised training strategy that outperforms previous supervised VCL methods. The FiGVCL benchmark dataset, metric, and baseline models will be available at https://github.com/vslab-vcl/FiGVCL.